The present invention, in some embodiments thereof, relates to the field of medical image data segmentation, and more particularly, to semi-automatic spatial segmentation of anatomical structures including, for example: lymph nodes, cysts, tumors, nodules and/or other lesions on three-dimensional (3D) medical image data.
Lymph nodes, in particular, are bean-shaped organs that play a critical role in the proper functioning of the immune system. They are widely distributed throughout the body, with a primary function to recognize and filter out foreign substances. Lymph nodes become inflamed or enlarged under pathological conditions ranging from mild infections, such as common cold, to life-threatening diseases, such as cancers. Therefore, lymph nodes are considered routinely in clinical practice. The quantitative assessment of their size over time is crucial in monitoring disease progress and treatment effectiveness.
Lymph nodes, cysts, tumors, nodules, lesions and other anatomical structures are typically analyzed on three-dimensional (3D) medical images produced by scanning technologies such as computed tomography (CT) and magnetic resonance (MR). These allow non-invasive imaging of internal organs and tissues. Currently available scanners provide a high spatial resolution suitable for accurate size measurement. However, in current clinical routines, radiologists estimate the size of anatomical structures on medical images manually, such that a large portion of this information may go unused. For example, estimation is based on approximate measures of the longest diameter and/or the short-axis on a 2D slice; a method recommended, for example by RECIST [1-2].
Challenges of lymph node spatial segmentation and volumetric analysis include: (i) lymph nodes are found in many different tissue environments throughout the body; (ii) lymph node image intensity values overlap with other soft tissues, such as muscles and vessels; and (iii) particularly when enlarged, lymph nodes possess a variety of shapes, textures, and sizes (for example, from 5 mm up to 50 mm or more in length). In many cases, such challenges are also found in the segmentation of other soft tissue structures, in particular cysts, nodules, tumors and other lesions.